Abstract
This paper analyzes the performance of the human localization by a spiking neural network in informationally structured space based on sensor networks. First, we discuss the importance of information structuralization. Next, we apply a spiking neural network to extract the human position in a room equipped with sensor network devices. Next, we propose how to update the base value as a method of preprocessing to generate input values to the spiking neurons, and the learning method of the spiking neural network based on the time series of measured data. Finally, we show several experimental results, and discuss the effectiveness of the proposed method.
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Tang, D., Kubot, N. (2010). Human Localization by Fuzzy Spiking Neural Network Based on Informationally Structured Space. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_4
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DOI: https://doi.org/10.1007/978-3-642-17537-4_4
Publisher Name: Springer, Berlin, Heidelberg
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